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<center><h1>Module <a href="type_MlPerceptron.html">MlPerceptron</a></h1></center>
<br>
<pre><span class="keyword">module</span> MlPerceptron: <code class="code">sig</code> <a href="MlPerceptron.html">..</a> <code class="code">end</code></pre>Implements a multilayer perceptron<br>
<hr width="100%">
<pre><span class="keyword">exception</span> <a name="EXCEPTIONWrong_input_size"></a>Wrong_input_size</pre>
<pre><span class="keyword">exception</span> <a name="EXCEPTIONWrong_init_sizes"></a>Wrong_init_sizes</pre>
<pre><span class="keyword">val</span> <a name="VALfeed_layer"></a>feed_layer : <code class="type">(float -> float) -> float array -> <a href="NeuralNetwork.html#TYPElayer">NeuralNetwork.layer</a> -> float array</code></pre><div class="info">
<code class="code">feed_layer threshold_function input layer</code>
	returns the output of the layer<br>
</div>
<pre><span class="keyword">val</span> <a name="VALfeed_and_sum_layer"></a>feed_and_sum_layer : <code class="type">(float -> float) -><br>       float array -> <a href="NeuralNetwork.html#TYPElayer">NeuralNetwork.layer</a> -> float array * float array</code></pre><div class="info">
<code class="code">feed_and_sum_layer threshold_function input layer</code><br>
<b>Returns</b> (activation, output) where<ul>
<li>activation : product of input and layer</li>
<li>output : array of the output values</li>
</ul>
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<pre><span class="keyword">class</span> <a name="TYPEmlPerceptron"></a><a href="MlPerceptron.mlPerceptron.html">mlPerceptron</a> : <code class="type">int -> int -> float Matrix.matrix array -> <a href="NeuralNetwork.html#TYPEvector">NeuralNetwork.vector</a> array -> <a href="NeuralNetwork.html#TYPEactivation">NeuralNetwork.activation</a> -> </code><code class="code">object</code> <a href="MlPerceptron.mlPerceptron.html">..</a> <code class="code">end</code></pre><div class="info">
Mutable sigmoid perceptron object
</div>
<pre><span class="keyword">type</span> <a name="TYPEt"></a><code class="type"></code>t = <code class="type"><a href="MlPerceptron.mlPerceptron.html">mlPerceptron</a></code> </pre>

<pre><span class="keyword">val</span> <a name="VALcreate"></a>create : <code class="type">int -><br>       int -><br>       int array -><br>       float -> float -> <a href="NeuralNetwork.html#TYPEactivation">NeuralNetwork.activation</a> -> <a href="MlPerceptron.mlPerceptron.html">mlPerceptron</a></code></pre><div class="info">
<code class="code">create input_size output_size layers_sizes weight bias activation</code>
	creates a sigmoid multi-layer perceptron where<ul>
<li>layers_sizes : array of the sizes of the successive layers</li>
<li>all weights are equal to weight, all biases to bias</li>
</ul>
<br>
</div>
<pre><span class="keyword">val</span> <a name="VALrandom"></a>random : <code class="type">int -><br>       int -><br>       int array -><br>       float -> float -> <a href="NeuralNetwork.html#TYPEactivation">NeuralNetwork.activation</a> -> <a href="MlPerceptron.mlPerceptron.html">mlPerceptron</a></code></pre><div class="info">
<code class="code">random input_size output_size layers_sizes weight_range bias_range</code>
	creates a sigmoid perceptron where<ul>
<li>layers_sizes : array of the sizes of the successive layers</li>
<li>weights_range : range of the random weights values around zero</li>
<li>bias_range ; range of the biases values around zero</li>
</ul>
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